Are doctors bribed by pharma? An analysis of data

By Rafael Fonseca MD & John A Tucker MBA, PhD

A Critical Analysis of a Recent Study by Hadland and colleagues

Association studies that draw correlations between drug company-provided meals and physician prescribing behavior have become a favorite genre among advocates of greater separation between drug manufacturers and physicians. Recent studies have demonstrated correlations between acceptance of drug manufacturer payments and undesirable physician behaviors, such as increased prescription of promoted drugs. The authors of such articles are usually careful to avoid making direct claims of a cause-effect relationship since their observations are based on correlation alone. Nonetheless, such a relationship is often implied by conjecture. Further, the large number of publications in high profile journals on this subject can only be justified by concerns that such a cause-and-effect relationship exists and is widespread and nefarious. In this article, we will examine a recent paper by Hadland et al. which explores correlational data relating opioid prescribing to opioid manufacturer payments and in which the authors imply the existence of a cause-and-effect relationship.1

We propose the relationship between transactions between the private sector (e.g., meals provided, consulting payments) and prescribing habits can fall into one of three categories:

Type Effect Comments
0 There is no cause-effect relationship between these transactions and prescribing habits. Correlative observations may merely be reflections of practice patterns, and likelihood to use a drug category. No harm exists.
Ia There is a demonstrable cause-effect for transactions and prescribing patterns. However, this relationship is associated with increased use of drugs that have been proven to be an improvement over the current standard. The effect is beneficial for patients. “Beneficial marketing.”
Ib An adverse causative effect is documented with establishment of causation. There is a possibility of patient harm. Patient harm occurs because the wrong medication is administered and contravenes medical standards. A minor damage is done but arguably exists, if a physician prescribes a more expensive medication when a cheaper alternative exists.

“Nefarious marketing.”

Hadland et al.: Opioid Prescriptions and Manufacturer Payments to Physicians

The authors of this paper linked physician-level data from the 2014 CMS Open Payments database to 2015 opioid prescribing behavior described in the Medicare Opioid Prescribing Database. They explored the hypothesis that meals and other payments increase physician opioid prescribing by examining the association between receipt of meals and other financial benefits with the number of opioid prescriptions written[1]. Specifically, they found the following:

  1. A nearly linear relationship between the number of opioid manufacturer-provided meals accepted by a prescriber and the number of opioid prescriptions written. The relevant data is provided in Figure 1 below. Prescribers who received nine meals from opioid manufacturers in 2014 prescribed opioid analgesics at slightly more than 3x the rate of those who accepted only one meal.
  2. When broken down by physician specialty, those who accepted any payment from opioid manufacturers wrote between 1.2% more and 11% more opioid prescriptions as those who did not accept any such payments (Table 1).

Figure 1.

Figure reproduced from JAMA Internal Medicine 2018, volume 178, 861-3 under the Fair Use provisions of Section 107 of the U.S. Copyright Act.

Table 1.

Table reproduced from JAMA Internal Medicine 2018, volume 178, 861-3 under the Fair Use provisions of Section 107 of the U.S. Act.

Hadland et al. conclude that

Amidst national efforts to curb the overprescribing of opioids, our findings suggest that manufacturers should consider a voluntary decrease or complete cessation of marketing to physicians. Federal and state governments should also consider legal limits on the number and amount of payments.

While no cause-and-effect relationship between payments and prescribing habits has been demonstrated by this correlative study, the implication that one exists is made clear in the authors’ recommendations. In our analysis below, we attempt a deeper dive to determine whether such a cause-and-effect relationship exists.

Our View: It is More Complicated than That….

To better understand the issues presented by the Hadland’s correlative study, we undertook an independent analysis of the same data. We repeated the Hadland data extraction from the CMS sources cited in the paper. We associated payments with prescribing behavior using physician name and geographical information as described by Hadland. Despite the lack of detail provided in the publication, we closely reproduced the number of opioid prescribers, the number of opioid prescribers accepting payments, and the total number of payments described in the Hadland paper. The only discrepancy we found between our data and that reported by Hadland is that we found a more substantial total payment amount of $13.1M vs. the $9.1M reported by Hadland et al. We found no simple explanation for this discrepancy, as the total payment amount was consistently about 50% higher than that described by Hadland when stratified by source or by payment type. While we are not able to firmly assess the source of this difference given the lack of a detailed protocol in the paper, we believe that part of the difference may have arisen by including a more comprehensive range of opiate products in our analysis relative to that used by Hadland.

How Large is the Association Between Manufacturer Payments and Prescribing Volume?

Our first criticism of the Hadland analysis is directed at the non-standard presentation of the data in Figure 1. The most widely accepted way to present the relationship between two continuous variables such as payments and the prescription count is a correlation diagram. We present the data in this manner in Figure 2 (Note the logarithmic Y axis). Doctors who accepted no free meals from opioid manufacturers wrote between 0 and 1000 opioid prescriptions in 2015. As did those who accepted 50 or more.

Figure 2. Correlation Diagram Relating Number of Opioid Prescriptions Written to Number of Drug Maker Meals Accepted

This graph gives a very different impression than the presentation of the same data in Figure 1. Why is that? Here we have shown every data point, though some are hard to see because there are so many of them (345K to be exact). In Hadland’s presentation of the data, they grouped the prescribers into categories based on the number of meals that they accepted. They calculated the mean for each group, which hides the tremendous variation in prescribing behavior within each group. The error bars are shown in Hadland’s figure are not standard deviations (a measure of within-group variation) but standard errors (A measure of how precisely the mean has been estimated). The latter value is derived from the former by dividing by the square root of the number of data points, which ranges as high as 8468 for some of the categories in Hadland’s figure. So a clear representation of within-group variation would show error bars as much as 92-fold larger than those shown.

A similar criticism can be directed at the presentation of the data in Table 1. Comparing mean prescribing rates between those who accepted any payment and those who accepted none gives a non-representative picture because the distributions are highly skewed. Imagine a cancer trial in which 5 patients live 2, 3, 3, 4, or 20 months. Reporting that the average survival was 7.5 months and the standard deviation was 8.3 months really doesn’t give a very meaningful picture of what happened in the trial. Similarly, Hadland et al. report that physicians who accepted payments in 2014 wrote 539 +/- 945 prescriptions in 2015, while those who did not wrote 134 +/- 281. Who are the physicians who wrote less than zero prescriptions in 2015, and what does a negative prescription look like? This type of bizarre result arises from applying statistical methods appropriate to a normal distribution of values to a data set that is decidedly non-normal.

The problems become even more apparent when we compare these numbers to the authors’ statement in the text that those who accepted payments in 2014 increased their prescription count in 2015 by 1.6, while those who did not accept payments in 2014 reduced their prescription count by 0.8. How is the difference (2.4 prescriptions) equal to 9.3% of 134 prescriptions (Table 1)? And doe a relative increase of 2.4 prescriptions per year from a base of 539 prescriptions merit publication in JAMA Internal Medicine and a call for legislation?

Are Drug Companies Paying Doctors to Write Prescriptions?

While the correlation between meals and opioid prescriptions is much weaker than implied by the figures presented in Hadland et al., a reasonable person might still object that ANY exchange in which prescriptions result from a conscious or unconscious quid pro quo for free lunches is morally unacceptable (Type Ib). We would certainly take that position. So let’s analyze whether the relationship is causative or merely correlative. Hadland’s implicit hypothesis is that doctors are writing opioid prescriptions in “exchange for pizza.” An alternative explanation might be that attending manufacturer informational sessions at which meals are served and prescribing opioids might both be driven by having a practice that involves treating many pain patients. Let’s look at the data and see if we can distinguish between these possibilities.

  • If Doctors are writing prescriptions in exchange for payments, one would expect that the number of prescriptions would rise predictably with the payment amount.

In practice, we find this is not the case.

Regressing the number of opioid prescriptions written on total payments received, we find r2 for the correlation is 0.01. Thus only 1% of the total variation amongst prescribers is associated with variation in the amount of payment received. (The gap in the graph between $0 and $10 arises because CMS does not require reporting of payments below $10).

Figure 3. Relationship Between the Number of Opioid Prescriptions Written and Total Payments Received

  • If doctors are writing prescriptions as quid pro quo for industry payments, one would expect that non-meal payments would show a correlation with prescribing similar to the correlation with meals shown in Figure 1.

Alternatively, if both attendance at educational sessions at which meals are served and opioid prescribing are driven by having a practice that involves treating many pain patients, one might expect a very modest or no correlation of prescribing with non-meal payments.

In practice, we see the latter (Figure 4).

Figure 4 was drawn using Hadland’s categorical style of presentation to allow direct comparison to Figure 1. While Hadland found that opioid prescribing tripled as the number of industry-sponsored meals increased from one to nine, we find no trend in toward increased prescribing among those who received between $0.01 and $65,536 in non-meal payments from opioid manfacturers. In fact, the geometric mean rate was nearly identical for those receiving less than $1 in non-meal payments (711 prescriptions) and for those receiving $32,000 to $64,000 (718 prescriptions). For the 58 physicians who received more than $65,536, the rate of prescribing was increased by nearly twofold relative to those receiving less than a dollar, but due to large within group differences, this difference was not statistically significant.

The fact that opioid prescribing correlates with the number of meals accepted but not with the total amount of non-meal payments received suggests that attendance at educational events at which meals are served and opioid prescribing are both driven by practice characteristics. In contrast, these data are difficult to accommodate within the theory that the association of prescribing rates with meals accepted is due to quid pro quo, or that companies are bribing doctors to prescribe their products.

Figure 4. Geometric Mean Prescribing Rates by Total Non-Meal Payments Received

  • If doctors are writing prescriptions in exchange for free meals, one would not expect meals provided by the manufacturer of non-opioid pain treatment to be associated with increased opioid prescribing. If doctors with large pain practices are more likely to attend informational lunches about pain products, such an association is expected and natural.

In practice, we find that the association of increased opioid prescribing with attendance at informational lunches offered by the manufacturers of pain therapeutics is independent of whether the pain product is an opioid!

St. Jude Medical is a medical device company that sells neuromodulation devices for the treatment of chronic pain. Those who attended St. Jude lunches prescribed opioids at the same rate as doctors who attended an equal number of lunches sponsored by opioid manufacturers. This observation holds up equally well when looking only at those who attended St. Jude lunches but did not attend any opioid lunches. We found similar associations with lunches provided by manufacturers of other non-opioids products (data not shown).

Figure 5. Relationship Between Attendance at Industry-Sponsored Lunches and Opioid Prescribing: St. Jude vs. Opioid Manufacturers

Conclusion

Correlation is not causation. While many advocates of reduced interactions between “commercial” interests and physicians have implied or directly suggested a quid pro quo between industry meals and other financial interactions and prescribing habits, correlation alone does not prove a quid pro quo relationship. In the case of opioid prescribing, we believe that we have presented a strong case that 1) the relationship between industry payments and prescribing is much weaker than has been presented in the literature, and 2) that prescribing and attendance at manufacturer-sponsored informational lunches are both driven by practice characteristics, rather than the meals themselves driving prescriptions (Type 0 relationship).

We believe that much of what has been published regarding the correlation of prescribing with industry payments and sponsored meals suffers from the shortcomings described in this short note. In particular, many of these papers conflate causation with correlation. In cases where fairly simple and obvious analyses would serve to differentiate between the authors’ preconceptions and alternative interpretations of the data, these analyses have not been performed. We urge all with an interest in this area to approach these data with the highest possible level of objectivity, as is our responsibility as scientists. We have done our best to do so here, and commit to doing so in our planned analyses of other papers in this area.

We look forward to a stimulating debate with those who have other data bearing on this issue, or other interpretations of the data presented herein.

References

Hadland SE, Cerdá M, Li Y, Krieger MS, Marshall BL. Association of pharmaceutical industry marketing of opioid products to physicians with subsequent opioid prescribing. JAMA internal medicine. 2018

[1]. This analysis, as well as alternative analyses performed by the present authors, was limited to the prescribing behavior of those who wrote at least ten opioid prescriptions in 2015 due to redaction of counts between 1 and ten by CMS.

About the authors

Rafael Fonseca is a hematologist at the Mayo Clinic in Arizona and John is a medicinal chemist residing in Northern California.

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The Rise of Litigation in the US

By SAURABH JHA

How did Americans become so litigious? What is the rationale of tort? Will tort reform work? I discuss these questions with Walter Olson, senior scholar at the Cato Institute, and author of the book, Litigation Explosion. 

Listen to our discussion on the Radiology Firing Line Series, hosted by the Journal of the American College of Radiology, and sponsored by Healthcare Administrative Partners.

The Rise of Litigation in the US published first on https://drugtestsblog.tumblr.com/

Is Medical Imaging a Ricardian ” Derived Demand”?

By SAURABH JHA

Medical Imaging and the Price of Corn

After the Napoleonic wars, the price of corn in England became unaffordable. The landowners were blamed for the high price, which some believed was a result of the unreasonably high rents for farm land. Economist David Ricardo disagreed.

According to Ricardo, detractors had the directionality wrong. It was the scarcity of corn (the high demand relative to its supply) that induced demand for the most fertile land. That is, the rent did not increase the price of corn. The demand for corn raised the rent. Rent was a derived demand.

Directionality is important. Getting directionality wrong means crediting the rooster for sunrise and blaming umbrellas for thunderstorms. It also means that focusing on medical imaging will not touch healthcare costs if factors more upstream are at play.

Medical imaging is a derived demand. The demand for healthcare induces demand for imaging. Demand is assured by the unmoored extent to which we go for marginal increases in survival.

The Demand for Imaging in Stroke

The treatment of ischemic stroke using thrombolytics and intra-arterial therapy (IAT) is instructive on how imaging can be induced. Lytics improve outcomes but must be administered relatively rapidly after onset of symptoms. IAT, which is treatment at the site of arterial blockage, allows the clock to tick for a bit longer and has recently been shown to be beneficial.

In the MR CLEAN[1] study, patients with acute ischemic stroke with radiographically proven occlusion in the proximal anterior circulation were randomly assigned to IAT or usual care. IAT included local thrombolysis, mechanical break up of thrombus, or stent placement. In both treatment and control groups, most patients also received thrombolysis with alteplase (Activase®).

The results published in the New England Journal of Medicine[1] showed that patients who received IAT within 6 hours of stroke onset had a clinically significant increase in functional independence at 3 months without higher mortality.

Counting the CTs and CT Angiograms

However, patients don’t walk into the emergency department saying, “Good evening, doctor, I have an occlusion in the proximal internal carotid artery. Can we get moving, please?” Sometimes they have classic signs and symptoms of stroke in the distribution of the target artery. Often they have more vague signs and symptoms that could be due to other causes.

(Read more here)

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Information Blocking–The AHA Comments & PPR Responds

The focus on the CMS rules on information blocking continues on THCB. We’ve heard from Adrian Gropper & Deborah Peel at Patients Privacy Rights, and from e-Patient Dave at SPM and Michael Millenson. Now Adrian Gropper summarizes — and in an linked article –notates on the American Hospital Association’s somewhat opposite perspective–Matthew Holt

It’s “all hands on deck” for hospitals as CMS ponders the definition and remedies for 21st Century Cures Act information blocking.

This annotated excerpt from the recent public comments on CMS–1694–P, Medicare Program; Hospital Inpatient Prospective Payment Systems…  analyzes the hospital strategy and exposes a campaign of FUD to derail HHS efforts toward a more patient-centered health records infrastructure.

Simply put, patient-directed health records sharing threatens the strategic manipulation of interoperability. When records are shared without patient consent under the HIPAA Treatment, Payment and Operations the hospital has almost total control.Patient and physician advocates like Patient Privacy Rights propose a patient-directed approach to national-scale health records sharing as a safe harbor for hospitals at risk of “information blocking”.  The consented approach, analogous to how our money is moved, avoids many privacy problems, does not require patient-matching, and avoids the hereto insurmountable problem of trust across the very diverse participants that make up a patient’s real-world care team.

The HITECH strategy of the previous Administration was markedly hospital-centered and has resulted in rising costs due to provider consolidation, a stunning lack of innovation in either technology or medical practice, and “burnout” on the part of disempowered physicians.

The bipartisan 21st Century Cures Act offers the current Administration the opportunity to change strategy in favor of a patient and physician-centered health IT architecture that regulates the interface (the Open API) rather than the software (the EHR). Needless to say, the hospitals would rather double-down on the current policies and continue to slow-walk any changes that might lead to practice innovation.

The linked document debates;

  • Conditions of Participation in Medicare as a payment-based stick instead of the hospital-centered carrots of the previous strategy;
  • The implementation of the Trusted Exchange Framework and Common Agreement (TEFCA) to fulfill 21st Century Cures mandates;
  • The definition of Information Blocking and the potential for a safe harbor when hospitals enable opt-in patient-directed sharing “without special effort”;
  • Avoiding a compromise between security and interoperability by implementing the recommendations of the API Task Force;
  • Using the new Medicare Blue Button 2.0 as a model for how institutions can share patient-level data cost-effectively, with each other;

The American Hospital Association has done all of us a great service by summarizing the laws and clearly arguing their position in a relatively succinct and readable manner. By using the comment format in a public Google document, PPR is keeping the structure of the AHA argument and offering an alternative recommendation, concisely.

ONC is hosting the 2nd Interoperability Forum on August 6th- 8th, 2018 (accessible online) and CMS is taking yet another round of public comment on some of this through September 10. Let us continue this exploration through your comments right here in THCB.

Adrian Gropper MD is CTO of Patient Privacy Rights

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Our Nordstrom Anniversary Sale Top Picks

The Nordstrom Anniversary Sale is in full swing and Team Fit Foodie took a trip to find some Nordstrom sale staples. We found great dresses for wedding season, the perfect booties for fall, cardigans for every season, and lots of great athleisure pieces. Check them out now! Do you guys have one season of the…

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Peanut Butter Banana Baked Oatmeal

Baked oatmeal queens do we have a recipe for you! This Peanut Butter Banana Baked Oatmeal is perfectly sweetened with mashed banana and a little bit of maple syrup and gluten-free made with ground oat flour and rolled oats. Make this on the weekend for the family or during the week for a healthy breakfast meal prep recipe.

Baked oatmeal queens do we have a recipe for you! This Peanut Butter Banana Baked Oatmeal is perfectly sweetened with mashed banana and a little bit of maple syrup and gluten-free made with ground oat flour and rolled oats. Make this on the weekend for the family or during the week for a healthy breakfast…

Read More »

The post Peanut Butter Banana Baked Oatmeal appeared first on Fit Foodie Finds.

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What’s Next in Health Tech Investment? 500 Startups VC Marvin Liao Weighs In

What do health tech investors think is ‘hot’ these days? Where is the money going? I ran into Marvin Liao, partner at 500 Startups (a VC fund/accelerator program that has made more than 2000 investments in early-stage tech startups over the past eight years) at ICEE Health in Bucharest, Romania, last month and had a chance to ask.

With refreshing candor, Marvin weighs in on whether or not digital therapeutics, mental health, and biotech have room to grow — and if Apple, Google, and Amazon really have the power to change the future of health.

Where is he most bullish? It’s no surprise I ran into him outside the US. He’s got his eyes on bleeding edge innovations coming out of foreign markets…especially Japan. Have a look!

Filmed at ICEE Health in Bucharest, Romania, June 2018. Find more interviews about health & technology at www.wtf.health

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